To identify successful strategies, Harford argues that “we should not try to design a better world. We should make better feedback loops” (140) so that failures can be identified and successes capitalized on. Harford just asserts that “a market provides a short, strong feedback loop” (141), because “If one cafe is ordering a better combination of service, range of food, prices, decor, coffee blend, and so on, then more customers will congregate there than at the cafe next door“, but everyday small-scale examples like this have little to do with markets for credit default swaps or with any other large-scale operation.

Yes, indeed.  The lead-time between undertaking initial business planning in order to raise early capital investments and the launching of services to the  public for  global satellite communications networks is in the order of 10 years (since satellites, satellite networks and user devices need to be designed, manufactured, approved by regulators, deployed, and connected before they can provide service).  The time between initial business planning and the final decommissioning of an international gas or oil pipeline is about 50 years.  The time between initial business planning and the final decommissioning of an international undersea telecommunications cable may be as long as 100 years.   As I remarked once previously, the design of Transmission Control Protocol (TCP) packets, the primary engine of communication in the 21st century Internet, is closely modeled on the design of telegrams first sent in the middle of the 19th century.  Some markets, if they work at all, only work over the long run, but as Keynes famously said, in the long run we are all dead.

I have experience of trying to design telecoms services for satellite networks (among others), knowing that any accurate feedback for design decisions may come late or not at all, and when it comes may be vague and ambiguous, or even misleading.   Moreover, the success or failure of the selected marketing strategy may not ever be clear, since its success may depend on the quality of execution of the strategy, so that it may be impossible to determine what precisely led to the outcome.   I have talked about this issue before, both regarding military strategies and regarding complex decisions in general.  If the quality of execution also influences success (as it does), then just who or what is the market giving feedback to?

In other words, these coffees are not always short and strong (in Harford’s words), but may be cold, weak, very very slow in arriving, and even their very nature contested.   I’ve not yet read Harford’s book, but if he thinks all business is as simple as providing fmc (fast-moving consumer) services, his book is not worth reading.

Once again, an economist argues by anecdote and example.  And once again, I wonder at the world:  That economists have a reputation for talking about reality, when most of them evidently know so little about it, or reduce its messy complexities to homilies based on the operation of suburban coffee shops.

I have just learnt of the death last  month of Sol Encel, Emeritus Professor of Sociology at the University of New South Wales, and a leading Australian sociologist, scenario planner, and futures thinker.    I took a course on futurology with him two decades ago, and it was one of the most interesting courses I ever studied.  This was  not due to Encel himself, at least not directly, who appeared in human form only at the first lecture.

He told us he was a very busy man, and would certainly not have the time to spare to attend any of the subsequent lectures in the course.  Instead, he had arranged a series of guest lectures for us, on a variety of topics related to futures studies, futurology, and forecasting.  Because he was genuinely important, his professional network was immense and impressive, and so the guest speakers he had invited were a diverse group of prominent people, from different industries, academic disciplines, professions, politics and organizations, each with interesting perspectives or experiences on the topic of futures and prognosis.  The talks they gave were absolutely fascinating.

To accommodate the guest speakers, the lectures were held in the early evening, after normal working hours.  Because of this unusual timing, and because the course assessment comprised only an essay, student attendance at the lectures soon fell sharply.  Often I turned up to find I was the only student present.   These small classes presented superb opportunities to meet and talk with the guest speakers, conversations that usually adjourned to a cafe or a bar nearby.  I learnt a great deal about the subject of forecasting, futures, strategic planning, and prognosis, particularly in real organizations with real stakeholders, from these interactions.  Since he chose these guests, I thus sincerely count Sol Encel as one of the important influences on my thinking about futures.

Here, in a tribute from the Australian Broadcasting Commission, is a radio broadcast Encel made in 1981 about Andrei Sakharov. It is interesting that there appears to have been speculation in the West then has to how the so-called father of the Soviet nuclear bomb could have become a supporter of dissidents.   This question worried, too, the KGB, whose answer was one Vadim Delone, poet.  And here, almost a month after Solomon Encel’s death, is his obituary in the Sydney Morning Herald.  One wonders why this took so long to be published.

Technorati Tags: Sol Encel, futurology

• The problems are not posed in a form amenable to classical decision theory.

  Decision theory requires the decision-maker to know what are his or her action-options, what are the consequences of these, what are the uncertain events which may influence these consequences, and what are the probabilities of these uncertain events (and to know all these matters in advance of the decision). Yet, for many real-world decisions, this knowledge is either absent, or may only be known in some vague, intuitive, way. The drug thalidomide, for example, was tested thoroughly before it was sold commercially – on male and female human subjects, adults and children. The only group not to be tested were pregnant women, which were, unfortunately, the main group for which the drug had serious side effects. These side effects were consequences which had not been imagined before the decision to launch was made. Decision theory does not tell us how to identify the possible consequences of some decision, so what use is it in real decision-making?

• There are fundamental domain uncertainties.
  

  None of us knows the future. Even with considerable investment in market research, future demand for new products may not be known because potential customers themselves do not know with any certainty what their future demand will be. Moreover, in many cases, we don’t know the past either. I have had many experiences where participants in a business venture have disagreed profoundly about the causes of failure, or even success, and so have taken very different lessons from the experience.

• Decisions may be unique (non-repeated).
  

  It is hard to draw on past experience when something is being done for the first time. This does not stop people trying, and so decision-making by metaphor or by anecdote is an important feature of real-world decision-making, even though mostly ignored by decision theorists.

• There may be multiple stakeholders and participants to the decision.
  

  In developing a business plan for a global satellite network, for example, a decision-maker would need to take account of the views of a handful of competitors, tens of major investors, scores of minor investors, approximately two hundred national and international telecommunications regulators, a similar number of national company law authorities, scores of upstream suppliers (eg equipment manufacturers), hundreds of employees, hundreds of downstream service wholesalers, thousands of downstream retailers, thousands or millions of shareholders (if listed publicly), and millions of potential customers. To ignore or oppose the views of any of these stakeholders could doom the business to failure. As it happens, Game Theory isn’t much use with this number and complexity of participants. Moreover, despite the view commonly held in academia, most large Western corporations operate with a form of democracy. (If opinions of intelligent, capable staff are regularly over-ridden, these staff will simply leave, so competition ensures democracy. In addition, good managers know that decisions unsupported by their staff will often be executed poorly, so success of a decision may depend on the extent to which staff believe it has been reached fairly.) Accordingly, all major decisions are decided by groups or teams, not at the sole discretion of an individual. Decision theorists, it seems to me, have paid insufficient attention to group decisions: We hear lots about Bayesian decision theory, but where, for example, is the Bayesian theory of combining subjective probability assessments?

• Domain knowledge may be incomplete and distributed across these stakeholders.
• Beliefs, goals and preferences of the stakeholders may be diverse and conflicting.
• Beliefs, goals and preferences of stakeholders, the probabilities of events and the consequences of decisions, may be determined endogenously, as part of the decision process itself.
  

  For instance, economists use the term network goods to refer to a good where one person’s utility depends on the utility of others. A fax machine is an example, since being the sole owner of fax is of little value to a consumer. Thus, a rational consumer would determine his or her preferences for such a good only AFTER learning the preferences of others. In other words, rational preferences are determined only in the course of the decision process, not beforehand.Having considerable experience in marketing, I contend that ALL goods and services have a network-good component. Even so-called commodities, such as natural resources or telecommunications bandwidth, have demand which is subject to fashion and peer pressure. You can’t get fired for buying IBM, was the old saying. And an important function of advertising is to allow potential consumers to infer the likely preferences of other consumers, so that they can then determine their own preferences. If the advertisement appeals to people like me, or people to whom I aspire to be like, then I can infer that those others are likely to prefer the product being advertized, and thus I can determine my own preferences for it. Similarly, if the advertisement appeals to people I don’t aspire to be like, then I can infer that I won’t be subject to peer pressure or fashion trends, and can determine my preferences accordingly.

  This is commonsense to marketers, even if heretical to many economists.

• The decision-maker may not fully understand what actions are possible until he or she begins to execute.
• Some actions may change the decision-making landscape, particularly in domains where there are many interacting participants.
  

  A bold announcement by a company to launch a new product, for example, may induce competitors to follow and so increase (or decrease) the chances of success. For many goods, an ecosystem of critical size may be required for success, and bold initiatives may act to create (or destroy) such ecosystems.

• Measures of success may be absent, conflicting or vague.
• The consequences of actions, including their success or failure, may depend on the quality of execution, which in turn may depend on attitudes and actions of people not making the decision.
  

  Most business strategies are executed by people other than those who developed or decided the strategy. If the people undertaking the execution are not fully committed to the strategy, they generally have many ways to undermine or subvert it. In military domains, the so-called Powell Doctrine, named after former US Secretary of State Colin Powell, says that foreign military actions undertaken by a democracy may only be successful if these actions have majority public support. (I have written on this topic before.)

• As a corollary of the previous feature, success of an action may require extensive and continuing dialog with relevant stakeholders, before, during and after its execution.
  

  This is not news to anyone in business.

• Success may require pre-commitments before a decision is finally taken.
  

  In the 1990s, many telecommunications companies bid for national telecoms licences in foreign countries. Often, an important criterion used by the Governments awarding these licences was how quickly each potential operator could launch commercial service. To ensure that they could launch service quickly, some bidders resorted to making purchase commitments with suppliers and even installing equipment ahead of knowing the outcome of a bid, and even ahead, in at least one case I know, of deciding whether or not to bid.

• The consequences of decisions may be slow to realize.
  

  Satellite mobile communications networks have typically taken ten years from serious inception to launch of service.  The oil industry usually works on 50+ year cycles for major investment projects.  BP is currently suffering the consequence in the Gulf of Mexico of what appears to be a decades-long culture which de-emphasized safety and adequate contingency planning.

• Decision-makers may influence the consequences of decisions and/or the measures of success.
• Intelligent participants may model each other in reaching a decision, what I term reflexivity.
  

  As a consequence, participants are not only reacting to events in their environment, they are anticipating events and the reactions and anticipations of other participants, and acting proactively to these anticipated events and reactions. Traditional decision theory ignores this. Following Nash, traditional game theory has modeled the outcomes of one such reasoning process, but not the processes themselves. Evolutionary game theory may prove useful for modeling these reasoning processes, although assuming a sequence of identical, repeated interactions does not strike me as an immediate way to model a process of reflexivity. This problem still awaits its Nash.

In my experience, classical decision theory and game theory do not handle these features very well; in some cases, indeed, not at all.  I contend that a new theory of complex decisions is necessary to cope with decision domains having these features.

Technorati Tags: game theory, decision theory, network goods, reflexivity

I spent the rest of my K-C career in advanced product development or new business identification, usually as a team leader, and sometimes as what Gifford Pinchot called an “Intrapreneur” – a corporate entrepreneur, driving new products from discovery to basis-for-interest to commercialization.  It’s the nature of many companies to prematurely dismiss ideas that represent what the world might want/need 5, 10 years out and beyond in favor of near-term opportunities – the intrapreneur stays under the radar, using passion, brains, intuition, stealth, any and every other human and material resource available to keep things moving.  It helps to have had some managers that often looked the other way.

. . .

As a senior product developer, and in the role of “intrapreneur” on Little Swimmers, I wore all the hats, technical and consumer, including hand-making many of the prototypes, making test product in machiladoras in July, performing materials testing, as well as the concept and positioning development work, consumer evaluations, whatever it took, taking the product through quantitative and volumetric concept-and-use testing and Basis For Interest.  When it came time for evaluations, the senior technical people who were my peers (PhD “Fellows”) did not regard the work as technically sophisticated enough for recognition or promotion in those ranks.  Since I was working for a senior business manager on the project, I didn’t have a technical champion for support.  I was caught in the middle.

There was simply no career path for the very senior level, creative, prolifically successful entrepreneurial product developer with a broad consumer-culture view. Business and Technical management were inappropriate (too right brained for me and actually took one out of the product development role), nor was the Fellow path appropriate (more for the highly technical).  I didn’t fit either path (this was true for other senior level product developers).  If my unique – and successful – methodologies and insights had been met with a broader willingness to understand, that might have offered some sense of fulfillment.”

Technorati Tags: Paula Rosch, Kimberly-Clark, product development

Let me start by repeating firmly what I said before (here abbreviated), as preface:

• Owners of companies (including the present British Government) have the legal and moral right to establish any legal payment policies they so wish. 
• People who fail should not be rewarded for that failure.

Then let me repeat this para:

Finally, in all the nonsense spoken about a “bonus culture” in banks, I have nowhere seen any discussion as to WHY bonus payments are common in the financial world.   The first reason is that financial markets are capricious:  despite all the boastful talk about skills and experience, and despite the multi-terabytes of computer memory, the massively-high bandwidths of connection, and the arrays of rocket scientists deployed, the taking of positions in markets is still a matter of taking views on the future, and betting these views against other views.   The future is uncertain, so bets can lose, as well as win,  and these two outcomes may happen regardless of the skills or expertise or resources applied to the taking of a view.  People may be just lucky or unlucky – even clever, experienced, well-resourced, cautious, nice people.    The second reason is that when bets win, they may win big.    If  a company makes hundreds of millions of dollars profit from a one or a handful of trades, it can seem most unfair to those deciding what trades to do that all of this capricious, windfall gain should accrue just to the shareholders.  Those doing the trading therefore ask, quite reasonably in my opinion, for a share of this windfall gain.   Most companies have then a choice:  give a few percent of these capricious windfalls to the staff doing the work as bonus payments, or risk losing the staff to the competitor down the street who will.   No rational, long-term manager would choose the latter option, and no rational, long-term shareholder would force him or her to.

There is another aspect to the business of the financial sector that I overlooked in this description.  For most of us working in companies, our actions have impacts on the revenues, costs and profits of the company only indirectly.  Typically, the larger the organization the more indirect the effects.  A product manager for one of the thousands of consumer products at, say, Proctor and Gamble, for instance may have great ideas for innovative product development, ideas which, when implemented, lead to massive increases in corporate revenues.  But she would not be able to develop these ideas, create the necessary support infrastructures (which may include product redesigns, or new manufacturing facilities, or new marketing campaigns) and then execute them, without help and support from a very large number of people; perhaps as many as tens of thousands of people would be involved for the most complex of products, from research scientists through to traveling salespeople in equatorial Africa.   Success in such environments requires real – and all too rare – skills at group-working, information sharing, socialization, co-ordination, complex project planning, and managing of others (in all directions, below, above and laterally), both inside one’s own company and in partner companies.  These abilities are required in addition to whatever linguistic and/or mathematical intelligence required (which can be considerable for any high-tech product and even for ordinary consumer durables) and so-called emotional intelligence.   This is a getting-things-done intelligence, a skill-set that otherwise very intelligent and successful people may lack, for example, academics, writers, medical professionals.

Strangely enough, few bankers, in my experience, need these getting-things-done abilities either, and few have them.  The large sums in finance, which generate the large bonuses, usually accrue (as I said above) to successful bets on the future.  To make such bets you need: (a) to take a view on the future (a view which may arise from sophisticated, complex and expensive computer modeling) and (b) money to bet with.  You don’t normally need a large team behind you, although computer modeling may require rare and specialised – and hence, expensive – skills. You don’t need much equipment, just a laptop and access to processing power (now rentable by the minute); you certainly don’t need a factory, or a global distribution network, or a boffin-filled R&D lab, as the product managers for P&G do.  Apart from the money used to bet with, you don’t need much money either.   You may need, as sociologist Donald MacKenzie has shown in his detailed fieldwork studies of financial institutions, a good network of contacts, both in your own organization and outside it, of people whom you trust you and who trust you; you need this network in order to be able to accurately predict financial market movements, and to predict how market participants would be likely to react to new information, in other words, to help you to take a view on the future.

So, suppose you work for a large financial institution in the City of London, betting on (say) US Dollar-to-Japanese Yen exchange rates.  You come into work, you read the newsfeeds, you look at the numbers, you talk with your network, you (or your team) do your computer simulations, you take a view of the future, and you place your bet.  (You may do this with any frequency, from every few weeks to every few minutes, depending on the market you are in and the strategies you running.)   The money you use to bet with belongs to your employer.  Perhaps you bet the value of the USD will rise in comparison to the Yen, as a result of the recent Japanese election.   Or perhaps you bet that most other exchange traders will bet that it will rise for this reason, and so you would profit from betting against them.  For whatever reason – good judgment, intuition, experience, clever algorithms, computer processing power, speed of reaction, personal network connectedness, luck, clever accounting, inside information, mojo, guidance from the beyond - your bets are successful, and thus you (and perhaps your small team) generate large revenues for your employer.   Unlike our friend at P&G, no one can say these revenues depended on the supportive and co-ordinated activities of hundreds or thousands of people working over months or years.  No one can say that your employer would have got the revenues anyway, even without you.  And given the few required inputs listed in the previous paragraph, no one can stop you walking out of your office one afternoon and offering your successful judgment (your view-taking ability) to some other employer with money to bet.  As the saying goes, the bank’s key assets walk out the front door each evening.

In other words, large bonuses exist in the financial world because large revenue streams exist which are directly traceable to the actions of a small number of people, and because not paying good bonuses to these people risks the employer losing the revenue streams.   None of this is evidence of malice, or evil, or some malcontent recalcitrance on the part of bankers, failing to reform despite the bailout.  It is the very nature of business under capitalism.  Employees are acting rationally and ethically in asking for such bonuses, and employers in giving them.  Large bonuses would exist in other industries if the conditions were right; indeed, they do, for senior managers in IPOs and acquisitions, or for Hollywood stars, able to guarantee large audiences for the opening weekends of films.  In both these cases, as with finance, large money streams can be traced directly to the actions of a small number of people.   If you were to make it illegal for banks to pay bonuses, then for consistency’s sake, you’d have to also tell JK Rowling to give up her millions.  Arguing that bonuses should not exist will be about as successful as arguing that water should not run downhill, and is, in my opinion, about as rational.

Reference:

Iain Hardie and Donald MacKenzie [2007]: Assembling an economic actor: the agencement of a Hedge Fund. The Sociological Review, 55 (1): 57-80.

But now the second generation of social networking forecasting tools has arrived.  I see that a French start-up, Doppio Software, has recently launched publicly.   They appear to have a product which has several advantages over ours:

• Doppio Software is focused on forecasting demand along a supply chain.  This means the forecasting objective is very tactical, not the long-term strategic forecasting that CO2 emission permit prices became.   In the present economic climate, short-term tactical success is certainly more compelling to business customers than even looking five years hence.
• The relevant social network for a supply chain is a much stronger community of interest than the amorphous groups we had in mind for Prophets-360.  Firstly, this community already exists (for each chain), and does not need to be created.  Secondly, the members of the community by definition have differential access to information, on the basis of their different positions up and down the chain.  Thirdly, although the interests of the partners in a supply chain are not identical, these interests are mutually-reinforcing:  everyone in the chain benefits if the chain itself is more successful at forecasting throughput.
• In addition, Team Doppio (the Doppiogangers?) appear to have included a very compelling value-add:  their own automated modeling of causal relationships between the target demand variables of each client and general macro-economic variables, using  semantic-web data and qualitative modeling technologies from AI.  Only the largest manufacturing companies can afford their own econometricians, and such people will normally only be able to hand-craft models for the most important variables.  There are few companies IMO who would not benefit from Doppio’s offer here.

Of course, I’ve not seen the Doppio interface and a lot will hinge on its ease-of-use (as with all software aimed at business users).  But this offer appears to be very sophisticated, well-crafted and compelling, combining social network forecasting, intelligent causal modeling and semantic web technologies.

Well done, Team Doppio!  I wish you every success with this product!

PS:  I have just learnt that “doppio” means “double”, which makes it a very apposite name for this application – forecasts considered by many people, across their human network.  Neat!  (2009-09-16)

Article in The Observer (UK) about Doppio 2009-09-06 here. And here is an AFP TV news story (2009-09-15) about Doppio co-founder, Edouard d’Archimbaud.  Another co-founder is Benjamin Haycraft.

Technorati Tags: Web 2.0, social-network forecasting, greenhouse gases, carbon emission, Doppio Software, causal modeling, semantic web technologies

Interestingly, season 1 of Mad Men had a couple of scenes involving market researchers, but the one woman was a PhD psychologist with a Central European accent, apparently unable to be creative and clearly instantiating a different (albeit then-common) archetype to Flo Skelly.

On Mad Men,  a reminder that Ta-Nehisi Coates, mashing Karl Rove, last October captured the demographic of the typical viewer with great precision:

Even if I’ve never met you, I know you all. You guys are that dude at the country club with the beautiful date, holding a martini and a cigarette, standing against the wall and making snide comments about all the CSI-viewers who pass by. And you’re also a Muslim. Can’t forget Muslim.

Technorati Tags: Mad Men, Flo Skelly

Now there is news that some of the chains are heading down-market, in order to take advantage of the recession.   But how to do this without losing your current market-position image, nor those customers still able and willing to pay premium prices?

Technorati Tags: class system, supermarket chains

What software architecture is appropriate for this mass of data?   Clearly, we’d want to store all the data, regardless of its format, in databases.  My question is pitched at a higher level of abstraction than that of the databases.  We desire that multiple, independent agents (both people and devices) are able to access the data, to read it and contribute to it, and maybe to over-write it (assuming they have the appropriate authorizations).  Morever, we want to be able to combine and reason-across the data generated by one spime, say a particular motor vehicle, with that of other spimes — say, other vehicles of the same model, or other vehicles owned by the same person, or other vehicles purchased in the same year, etc.   We’d also like to combine and reason-across the data generated by spimes in different product categories — all the durables purchased by the Smith family in their life, for instance, or all the products purchased in Main Street, Anytown, last week.

An obvious data architecture for multiple, independent reading- and writing-entities is a blackboard.  A blackboard architecture is a shared memory space which enables agents sending and receiving messages to be decoupled from one another, both spatially and temporally.   Exactly as a blackboard does, messages left on the blackboard are stored until they are erased, and so the long-dead can communicate to the living, who can in turn communicate to the not-yet-born.   Tuple spaces and the assocated Linda language are an example of a blackboard architecture (implemented in Java as Java Spaces).  We could imagine that each spime has its own tuple space, partitioned into secure sub-spaces for different spime-wranglers, from manufacturers, through each spime owner or carer, to after-sales service providers and disposal agencies.  Access to spaces will need to be controlled, so that only authorized agents may write, read and erase data in their allocated partition.   Here we could use something called Law-Governed Linda, an ehancement of Linda designed to add security features to Linda, although this may be too rigid for products whose uses cannot be readily predicted in advance.   An architecture allowing access to a tuple space following an appropriate dialogue between the relevant agents may be more flexible.

So far, so good for the data storage and access.  But spimes and spime wranglers will generate enormous quantities of data, and analyzing all this data will require some effort.  Better then, to plan for this effort and automate as much of the data collation, aggregation, processing and analysis.   Here, I suggest we should use so-called Tuple Centres, which are intelligent Tuple Spaces, able to reason over the data they hold.  Because we will want to combine and analyze data arising from different spimes, these tuple centres will need to communicate with one another, and agree (or not) to allow their data to be aggregated. A multi-agent system (MAS) with agents representing each spime-space (ie, the tuple space of each spime), and, for many spimes, each partition of each spime-space, seems the most effective architecture.  This is because the interests of the relevant stakeholders (spime-wranglers, marketing departments, manufacturers and service providers, data protection agencies, the state, the law) will vary and a MAS is the most effective way to formally represent and accommodate these diverse interests in a software system.

There are many details still be worked for this architecture.  But even at this level, it is clear that the traditional marketing data warehouse architecture is not sophisticated enough for what is needed for spimes. Hence, my statement above that spimes could lead to major changes in the way in which marketing data is collected, collated, stored and analyzed.  Use of spime data I will leave for another post.

References:

TuCSon, developed at the University of Bologna, Italy, is a platform which enables fast implementation of tuple centre applications.

Technorati Tags: marketing data, spimes, intelligent spimes, software architecture, data architecture, blackboard architecture, Linda, spime wranglers, multi-agent system, marketing data warehouse